Fabricated steel structure building multi-objective optimization design method and system

Abstract

The invention provides a multi-objective optimization design method and a multi-objective optimization design system for a fabricated steel structure building, and relates to the technical field of house buildings. Establishing a relation between the building parameters and the building cost and building energy consumption to obtain a building cost optimization relational expression and a building energy consumption optimization relational expression, and obtaining a multi-target building optimization relational expression, and solving the multi-target building optimization relational expression by using a multi-target genetic algorithm NSGA-I to obtain an optimal building parameter solution set; calculating the building structure stability of each group of optimal solutions in the optimal building parameter solution set; through a mode of comparing the stability of the building structure with a preset threshold value, eliminating an optimal solution of which the stability of the building structure does not reach the standard, and obtaining a feasible optimal building parameter solution set; and determining an ideal optimal solution according to the feasible optimal building parameter solution set, calculating the distance between each solution in the feasible optimal building parameter solution set and the ideal optimal solution to obtain an ideal distance, and determining the optimal building parameters according to the ideal distance.

Description

Technical Field

[0001] The present invention relates to the technical field of housing construction, and in particular to a multi-objective optimization design method and system for assembled steel structure buildings. Background Art

[0002] As the global urbanization process continues to advance, the construction industry faces unprecedented challenges and opportunities. Modern buildings not only need to meet people's growing housing and use needs, but also must take into account environmental protection and resource conservation. Prefabricated steel structure buildings have received widespread attention worldwide due to their advantages such as fast construction speed, high structural strength and recyclable materials. However, in the actual design process, architects and engineers often face multi-dimensional optimization problems, such as how to reduce energy consumption and ensure the stability and safety of the structure while reducing costs.

[0003] Traditional design methods usually adopt a single-objective optimization strategy, that is, they only focus on improving one aspect. The limitation of this method is that it cannot take into account other important indicators at the same time, resulting in the design scheme not being able to achieve the ideal state in terms of overall performance. For example, focusing only on cost may lead to increased energy consumption or weakened structure, while pursuing energy saving alone may lead to excessive cost increases. Therefore, there is an urgent need for an optimization method that can comprehensively consider multiple design objectives to achieve all-round performance improvement.

[0004] With the development of computing technology and intelligent algorithms, multi-objective optimization methods have gradually emerged in architectural design. These methods can generate more balanced design solutions by considering multiple interrelated design goals at the same time. Among them, the multi-objective genetic algorithm NSGA-II has become an ideal choice for solving the multi-objective optimization problem of architectural design due to its superior performance in dealing with complex multi-objective optimization problems.

[0005] By introducing this advanced optimization technology, the construction industry is expected to break through the limitations of traditional design, develop more energy-saving, efficient and economically viable building solutions, and promote building design in a more efficient and sustainable direction.

[0006] In the prior art, the publication number CN116341354A discloses a multi-objective optimization design method and platform for ultra-low energy consumption buildings with prefabricated steel structures. According to the structural characteristics of prefabricated steel structures, parametric design research and BIM dynamic model automatic modeling are carried out; considering the influence of the material parameters of the building envelope structure on the building energy consumption, a variety of artificial intelligence algorithms are used to complete the prediction of building energy consumption, and an efficient and accurate building energy consumption prediction method is obtained by comparison and variable importance analysis is performed; considering the bearing capacity constraints of the structure, a multi-objective optimization algorithm is used to perform multi-objective optimization design of ultra-low energy consumption buildings with prefabricated steel structures. At the same time, a multi-objective optimization design platform for ultra-low energy consumption buildings with prefabricated steel structures is constructed, which organically combines structural stability verification with embedded machine learning models and multi-objective optimization design, making the entire actual process more automated and intelligent. However, the prior art still has defects. The prior art only optimizes the total length, total width and total number of floors of the building, and lacks optimization of fine parameters, which will lead to problems such as waste of resources, increased costs, high energy consumption, poor comfort and unstable structure; secondly, the prior art lacks deep integration of building structure stability assessment, which affects the safety and practicality of the overall design.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0008] The object of the present invention is to provide a multi-objective optimization design method and system for prefabricated steel structure buildings to solve the problems raised in the above-mentioned background technology.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A multi-objective optimization design method and system for assembled steel structure buildings, the specific steps include:

[0011] Step 1: Automatically generate a prefabricated steel structure building that meets specific goals by adjusting different building lengths, building widths, and building heights. Generate the apartment layout of the entire prefabricated steel structure by adjusting the number of building floors, outer wall thickness, and inner wall thickness. Obtain a BIM model of the steel frame by assigning beam and column material properties.

[0012] Step 2: Respectively connect the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness with the construction cost and building energy consumption to obtain the construction cost optimization relationship and the building energy consumption optimization relationship;

[0013] Step 3: Determine the optimization direction according to the building cost optimization relation and the building energy consumption optimization relation to obtain a multi-objective building optimization relation, and use the multi-objective genetic algorithm NSGA-II to solve the multi-objective building optimization relation to obtain the optimal building parameter solution set;

[0014] Step 4: Calculate the building structure stability of each optimal solution in the optimal building parameter solution set; by comparing the building structure stability with a preset threshold, eliminate the optimal solutions that do not meet the building structure stability standard, and obtain a feasible optimal building parameter solution set;

[0015] Step 5: Determine the ideal optimal solution based on the feasible optimal building parameter solution set, calculate the ideal distance between each solution in the feasible optimal building parameter solution set and the ideal optimal solution, and determine the optimal building parameters based on the ideal distance.

[0016] Furthermore, the specific logic for obtaining the construction cost optimization relationship is: establish a relationship between the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness and construction cost to obtain the construction cost optimization relationship. The specific formula for obtaining the construction cost optimization relationship is:

[0017]

[0018] Among them, L(k, d, h, n, l1, l2) is the construction cost optimization relationship, Ex is the land price, k is the total length of the building, d is the total width of the building, h is the total height of the building, l1 is the thickness of the outer wall, l2 is the thickness of the inner wall, n is the number of building floors, N is the number of building rooms, c1 is the outer wall price evaluation factor, and c2 is the inner wall price evaluation factor.

[0019] Furthermore, the specific logic for obtaining the outer wall price evaluation factor is as follows: obtain the price and usage of each material of the l1*l1*l1 outer wall, add the product of the price and usage of each material to obtain the outer wall price evaluation factor, and the distance formula for obtaining the outer wall price evaluation factor is as follows:

[0020]

[0021] Among them, m1 is the total amount of outer wall materials, Co i is the price of the i-th outer wall material, Nm i is the amount of the i-th outer wall material, i is the type index of the outer wall material, and i∈[1,m1];

[0022] The specific logic for obtaining the inner wall price evaluation factor is as follows: obtain the price and usage of each material of the l2*l2*l2 inner wall, add the product of the price and usage of each material to obtain the outer wall price evaluation factor, and the distance formula for obtaining the inner wall price evaluation factor is as follows:

[0023]

[0024] Among them, m2 is the total amount of inner wall materials, Co j is the price of the jth outer wall material, Nm j is the amount of the jth outer wall material, j is the index of the inner wall material type, and j∈[1,m2];

[0025] Furthermore, the specific logic for obtaining the building energy consumption optimization relationship is: the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness and building energy consumption are linked to obtain the building energy consumption optimization relationship. The specific formula for obtaining the building energy consumption optimization relationship is:

[0026]

[0027] Among them, En(k,d,h,n,l1,l2) is the building energy consumption optimization formula, k is the total length of the building, d is the total width of the building, h is the total height of the building, l1 is the thickness of the outer wall, l2 is the thickness of the inner wall, n is the number of floors, P g1 is the outdoor atmospheric pressure at the center of the g-th floor of the building, P g2 It is the indoor atmospheric pressure at the center height of the g-th floor of the building.

[0028] Furthermore, the specific logic for obtaining the multi-objective building optimization relationship is: the optimization direction is determined according to the building cost optimization relationship and the building energy consumption optimization relationship to obtain the multi-objective building optimization relationship, and the specific formula is:

[0029]

[0030] Among them, F(k,d,h,n,l1,l2) is the multi-objective building optimization relation, L(k,d,h,n,l1,l2) is the building cost optimization relation, and En(k,d,h,n,l1,l2) is the building energy consumption optimization relation.

[0031] Furthermore, the specific logic for obtaining the feasible optimal building parameter solution set is as follows: calculating the building structure stability of each optimal solution in the optimal building parameter solution set; comparing the building structure stability with a preset building structure stability threshold, determining the solution whose building structure stability performance is less than the building structure stability threshold as unsatisfactory, eliminating the optimal solution whose building structure stability is unsatisfactory, and obtaining the feasible optimal building parameter solution set; the specific formula for calculating the building structure stability is as follows:

[0032]

[0033] Among them, Wd is the stability of the building structure, k is the total length of the building, d is the total width of the building, h is the total height of the building, l1 is the thickness of the outer wall, l2 is the thickness of the inner wall, n is the number of building floors, σ1 is the compressive strength of the steel material, τ is the shear strength of the steel material, We is the building weight, Cw is the wind load coefficient, and v is the local maximum historical wind speed.

[0034] Furthermore, the specific logic for determining the optimal building parameters is as follows: respectively calculate the building cost and building energy consumption of each solution in the optimal building parameter solution set, obtain the solution with the minimum building cost and building energy consumption, and use the smaller building parameter of the two solutions to form an ideal optimal solution, calculate the ideal distance between each solution in the feasible optimal building parameter solution set and the ideal optimal solution, and define the building parameters in the solution with the smallest ideal distance as the optimal building parameters. The specific formula for calculating the ideal distance is as follows:

[0035]

[0036] Among them, DJ t is the ideal distance of the tth feasible optimal building parameter solution, k t is the total length of the building for the tth feasible optimal building parameter solution, d t is the total width of the building for the tth feasible optimal building parameter solution, h t is the total building height of the tth feasible optimal building parameter solution, l 1t is the thickness of the outer wall of the tth feasible optimal building parameter solution, l 2t is the thickness of the inner wall of the tth feasible optimal building parameter solution, n t is the number of floors of the tth feasible optimal building parameter solution, k0 is the total length of the building of the ideal optimal solution, d0 is the total width of the building of the ideal optimal solution, h0 is the total height of the building of the ideal optimal solution, l 10 is the thickness of the outer wall of the ideal optimal solution, l 20 is the thickness of the inner wall of the ideal optimal solution, and n0 is the number of building floors of the ideal optimal solution.

[0037] The present invention further provides a multi-objective optimization design system for prefabricated steel structure buildings, the system being used to implement any step of the multi-objective optimization design method for prefabricated steel structure buildings, specifically comprising:

[0038] Framework modeling module: used to automatically generate prefabricated steel structure buildings that meet specific goals by adjusting different building lengths, building widths, and building heights. By adjusting the number of building floors, outer wall thickness, and inner wall thickness, the apartment layout of the entire prefabricated steel structure is generated. By assigning beam and column material properties, the BIM model of the steel frame is obtained.

[0039] Relationship building module: used to establish relationships between the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness, construction cost, and construction energy consumption, respectively, to obtain construction cost optimization relationship and building energy consumption optimization relationship;

[0040] Parameter solving module: used to determine the optimization direction according to the building cost optimization relation and the building energy consumption optimization relation to obtain the multi-objective building optimization relation, and use the multi-objective genetic algorithm NSGA-II to solve the multi-objective building optimization relation to obtain the optimal building parameter solution set;

[0041] Stability analysis module: used to calculate the building structure stability of each optimal solution in the optimal building parameter solution set; by comparing the building structure stability with the preset threshold, the optimal solution with substandard building structure stability is eliminated to obtain a feasible optimal building parameter solution set;

[0042] Parameter determination module: used to determine the ideal optimal solution based on the feasible optimal building parameter solution set, calculate the ideal distance between each solution in the feasible optimal building parameter solution set and the ideal optimal solution, and determine the optimal building parameters based on the ideal distance.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] In addition to macro-building parameters such as total length, total width, total height, and number of floors, the present invention also optimizes the thickness of the outer wall and the thickness of the inner wall, solving the problems of resource waste, increased costs, high energy consumption, poor comfort, and structural instability caused by the lack of fine parameter optimization.

[0045] The present invention also evaluates the stability of building parameters through formulas, eliminates substandard solutions, and ensures the safety of the design solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0047] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0049] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0050] Example:

[0051] See also Figure 1 , the present invention provides a technical solution:

[0052] A multi-objective optimization design method for assembled steel structure buildings, the specific steps include:

[0053] Step 1: Automatically generate a prefabricated steel structure building that meets specific goals by adjusting different building lengths, building widths, and building heights. Generate the apartment layout of the entire prefabricated steel structure by adjusting the number of building floors, outer wall thickness, and inner wall thickness. Obtain a BIM model of the steel frame by assigning beam and column material properties.

[0054] Step 2: Respectively connect the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness with the construction cost and building energy consumption to obtain the construction cost optimization relationship and the building energy consumption optimization relationship;

[0055] The specific logic for obtaining the optimal relationship of construction costs is: establish a relationship between the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness and construction costs to obtain the optimal relationship of construction costs. The specific formula for obtaining the optimal relationship of construction costs is:

[0056]

[0057] Among them, L(k,d,h,n,l1,l2) is the construction cost optimization relationship, Ex is the land price, k is the total length of the building, d is the total width of the building, h is the total height of the building, l1 is the thickness of the outer wall, l2 is the thickness of the inner wall, n is the number of floors, N is the number of rooms, c1 is the outer wall price evaluation factor, and c2 is the inner wall price evaluation factor. L(k,d,h,n,l1,l2) is the construction cost optimization relationship that reflects the relationship between construction costs and parameters to be optimized. The establishment of this relationship can provide an important basis for the subsequent optimization of construction parameters.

[0058] The specific logic for obtaining the outer wall price evaluation factor is: obtain the price and usage of each material of the l1*l1*l1 outer wall, add the product of the price and usage of each material to obtain the outer wall price evaluation factor, and the distance formula for obtaining the outer wall price evaluation factor is:

[0059]

[0060] Among them, m1 is the total amount of outer wall materials, Co i is the price of the i-th outer wall material, Nm i is the amount of the i-th outer wall material, i is the outer wall material type index, and i∈[1,m1]; the outer wall price evaluation factor c1 reflects the material cost required to build the outer wall. The larger its value, the more material cost is required to build the outer wall.

[0061] The specific logic for obtaining the inner wall price evaluation factor is as follows: obtain the price and usage of each material of the l2*l2*l2 inner wall, add the product of the price and usage of each material to obtain the outer wall price evaluation factor, and the distance formula for obtaining the inner wall price evaluation factor is as follows:

[0062]

[0063] Among them, m2 is the total amount of inner wall materials, Co j is the price of the jth outer wall material, Nm j is the amount of the jth outer wall material, j is the type index of the inner wall material, and j∈[1,m2]; the inner wall price evaluation factor c2 reflects the material cost required to build the inner wall. The larger its value, the more material cost is required to build the inner wall.

[0064] The specific logic for obtaining the building energy consumption optimization relationship is: establish a relationship between the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness and building energy consumption to obtain the building energy consumption optimization relationship. The specific formula for obtaining the building energy consumption optimization relationship is:

[0065]

[0066] Among them, En(k,d,h,n,l1,l2) is the building energy consumption optimization formula, k is the total length of the building, d is the total width of the building, h is the total height of the building, l1 is the thickness of the outer wall, l2 is the thickness of the inner wall, n is the number of floors, P g1 is the outdoor atmospheric pressure at the center of the g-th floor of the building, P g2 is the indoor atmospheric pressure at the center height of the g-th floor of the building. The building energy consumption optimization equation En(k, d, h, n, l1, l2) reflects the relationship between building energy consumption and the building parameters to be optimized. The establishment of this equation can provide an important basis for the subsequent optimization of building parameters. Buildings with different floors have different energy consumption. Generally, the higher the number of floors, the higher the building energy consumption. This embodiment quantifies the energy consumption of buildings with different floors by the atmospheric pressure inside and outside the buildings with different floors.

[0067] Step 3: Determine the optimization direction according to the building cost optimization relation and the building energy consumption optimization relation to obtain a multi-objective building optimization relation, and use the multi-objective genetic algorithm NSGA-II to solve the multi-objective building optimization relation to obtain the optimal building parameter solution set;

[0068] The specific logic for obtaining the multi-objective building optimization relationship is: determine the optimization direction according to the building cost optimization relationship and the building energy consumption optimization relationship to obtain the multi-objective building optimization relationship. The specific formula is:

[0069]

[0070] Among them, F(k,d,h,n,l1,l2) is the multi-objective building optimization relation, L(k,d,h,n,l1,l2) is the building cost optimization relation, and En(k,d,h,n,l1,l2) is the building energy consumption optimization relation.

[0071] M building parameter populations are randomly generated, and the optimization function value of each individual in the building parameter population is calculated. The individuals in the population are non-dominated sorted by the optimization function value, and the crowding degree of each individual is calculated. Each individual is selected, crossed and mutated by the non-dominated sorting and crowding degree, and the population is updated. The selection, crossover, mutation and population update operations are repeated until a preset number of iterations is reached, and the building parameter population after the iteration is non-dominated sorted. The Pareto front obtained by non-dominated sorting of the building parameter population after the iteration is used as the optimal building parameter solution set of the multi-objective building optimization relationship. This prior art will not be repeated here.

[0072] Step 4: Calculate the building structure stability of each optimal solution in the optimal building parameter solution set; by comparing the building structure stability with a preset threshold, eliminate the optimal solutions that do not meet the building structure stability standard, and obtain a feasible optimal building parameter solution set;

[0073] The specific logic for obtaining the feasible optimal building parameter solution set is as follows: calculate the building structure stability of each optimal solution in the optimal building parameter solution set; compare the building structure stability with the preset building structure stability threshold, judge the solution whose building structure stability performance is less than the building structure stability threshold as unsatisfactory, eliminate the optimal solution whose building structure stability is unsatisfactory, and obtain the feasible optimal building parameter solution set; the specific formula for calculating the building structure stability is as follows:

[0074]

[0075] Among them, Wd is the building structure stability, k is the total length of the building, d is the total width of the building, h is the total height of the building, l1 is the thickness of the outer wall, l2 is the thickness of the inner wall, n is the number of floors, σ1 is the compressive strength of the steel material, τ is the shear strength of the steel material, We is the building weight, Cw is the wind load coefficient, and v is the local maximum wind speed. The building structure stability Wd reflects the structural stability of the optimized parameters. The larger the value, the stronger the structural stability of the building corresponding to this set of parameters. The generation of the building structure stability Wd can eliminate substandard solutions and ensure the safety of the design scheme.

[0076] Step 5: Determine the ideal optimal solution based on the feasible optimal building parameter solution set, calculate the ideal distance between each solution in the feasible optimal building parameter solution set and the ideal optimal solution, and determine the optimal building parameters based on the ideal distance.

[0077] The specific logic for determining the optimal building parameters is: calculate the building cost and building energy consumption of each solution in the optimal building parameter solution set, and obtain the solution with the minimum building cost (k V ,d V ,h V ,n V ,l 1V ,l 2V ) and the minimum building energy consumption solution (k Y ,d Y ,h Y ,n Y ,l 1Y ,l 2Y ), the smaller building parameter of the two solutions constitutes the ideal optimal solution, calculates the ideal distance between each solution in the feasible optimal building parameter solution set and the ideal optimal solution, and defines the building parameter in the solution with the smallest ideal distance as the optimal building parameter. The specific formula for constituting the ideal optimal solution is:

[0078] k0=min(k V ,k Y )

[0079] d0=min(d V ,d Y )

[0080] h0=min(h V ,h Y )

[0081] n0=min(n V ,n Y )

[0082] l 10 =min(l 1V ,l 1Y )

[0083] l 20 =min(l 2V ,l 2Y )

[0084] The specific formula for calculating the ideal distance is:

[0085]

[0086] Among them, DJ t is the ideal distance of the tth feasible optimal building parameter solution, k t is the total length of the building for the tth feasible optimal building parameter solution, d t is the total width of the building for the tth feasible optimal building parameter solution, h t is the total building height of the tth feasible optimal building parameter solution, l 1t is the thickness of the outer wall of the tth feasible optimal building parameter solution, l 2t is the thickness of the inner wall of the tth feasible optimal building parameter solution, n t is the number of floors of the tth feasible optimal building parameter solution, k0 is the total length of the building of the ideal optimal solution, d0 is the total width of the building of the ideal optimal solution, h0 is the total height of the building of the ideal optimal solution, l 10 is the thickness of the outer wall of the ideal optimal solution, l 20 is the thickness of the inner wall of the ideal optimal solution, and n0 is the number of building floors of the ideal optimal solution.

[0087] The present invention further provides a multi-objective optimization design system for prefabricated steel structure buildings, the system being used to implement any step of the multi-objective optimization design method for prefabricated steel structure buildings, specifically comprising:

[0088] Framework modeling module: used to automatically generate prefabricated steel structure buildings that meet specific goals by adjusting different building lengths, building widths, and building heights. By adjusting the number of building floors, outer wall thickness, and inner wall thickness, the apartment layout of the entire prefabricated steel structure is generated. By assigning beam and column material properties, the BIM model of the steel frame is obtained.

[0089] Relationship building module: used to establish relationships between the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness, construction cost, and construction energy consumption, respectively, to obtain construction cost optimization relationship and building energy consumption optimization relationship;

[0090] Parameter solving module: used to determine the optimization direction according to the building cost optimization relation and the building energy consumption optimization relation to obtain the multi-objective building optimization relation, and use the multi-objective genetic algorithm NSGA-II to solve the multi-objective building optimization relation to obtain the optimal building parameter solution set;

[0091] Stability analysis module: used to calculate the building structure stability of each optimal solution in the optimal building parameter solution set; by comparing the building structure stability with the preset threshold, the optimal solutions that do not meet the building structure stability standard are eliminated to obtain a feasible optimal building parameter solution set;

[0092] Parameter determination module: used to determine the ideal optimal solution based on the feasible optimal building parameter solution set, calculate the ideal distance between each solution in the feasible optimal building parameter solution set and the ideal optimal solution, and determine the optimal building parameters based on the ideal distance.

[0093] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0094] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0095] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A multi-objective optimization design method for assembled steel structure buildings, characterized in that: The specific steps include: Step 1: Automatically generate a prefabricated steel structure building that meets specific goals by adjusting different building lengths, building widths, and building heights. Generate the apartment layout of the entire prefabricated steel structure by adjusting the number of building floors, outer wall thickness, and inner wall thickness. Obtain a BIM model of the steel frame by assigning beam and column material properties. Step 2: Respectively establish relationships between the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness, construction cost, and construction energy consumption to obtain construction cost optimization equations and construction energy consumption optimization equations; Step 3: Determine the optimization direction according to the building cost optimization relation and the building energy consumption optimization relation, obtain the multi-objective building optimization relation, use the multi-objective genetic algorithm NSGA-II to solve the multi-objective building optimization relation, and obtain the optimal building parameter solution set; Step 4: Calculate the building structure stability of each optimal solution in the optimal building parameter solution set; by comparing the building structure stability with a preset threshold, eliminate the optimal solutions that do not meet the building structure stability standard, and obtain a feasible optimal building parameter solution set; Step 5: Determine the ideal optimal solution based on the feasible optimal building parameter solution set, calculate the ideal distance between each solution in the feasible optimal building parameter solution set and the ideal optimal solution, and determine the optimal building parameters based on the ideal distance.

2. The multi-objective optimization design method for prefabricated steel structure buildings according to claim 1 is characterized in that: The specific logic for obtaining the construction cost optimization formula is: establish a relationship between the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness and construction cost to obtain the construction cost optimization formula. The specific formula for obtaining the construction cost optimization formula is: Among them, L(k, d, h, n, l1, l2) is the construction cost optimization relationship, Ex is the land price, k is the total length of the building, d is the total width of the building, h is the total height of the building, l1 is the thickness of the outer wall, l2 is the thickness of the inner wall, n is the number of building floors, N is the number of building rooms, c1 is the outer wall price evaluation factor, and c2 is the inner wall price evaluation factor.

3. The multi-objective optimization design method for prefabricated steel structure buildings according to claim 2 is characterized in that: The specific logic for obtaining the outer wall price evaluation factor is: obtain the price and usage of each material of the l1*l1*l1 outer wall, add the product of the price and usage of each material to obtain the outer wall price evaluation factor, and the distance formula for obtaining the outer wall price evaluation factor is: Among them, m1 is the total amount of outer wall materials, Co i is the price of the i-th outer wall material, Nm i is the amount of the i-th outer wall material, i is the index of the outer wall material type, and i∈[1,m2]; The specific logic for obtaining the inner wall price evaluation factor is as follows: obtain the price and usage of each material of the l2*l2*l2 inner wall, add the product of the price and usage of each material to obtain the outer wall price evaluation factor, and the distance formula for obtaining the inner wall price evaluation factor is as follows: Among them, m2 is the total amount of inner wall materials, Co j is the price of the jth outer wall material, Nm j is the amount of the jth outer wall material, j is the type index of the inner wall material, and j∈[1,m2].

4. The multi-objective optimization design method for prefabricated steel structure buildings according to claim 1 is characterized in that: The specific logic for obtaining the building energy consumption optimization relationship is: establish a relationship between the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness and building energy consumption to obtain the building energy consumption optimization relationship. The specific formula for obtaining the building energy consumption optimization relationship is: Among them, En(k,d,h,n,l1,l2) is the building energy consumption optimization formula, k is the total length of the building, d is the total width of the building, h is the total height of the building, l1 is the thickness of the outer wall, l2 is the thickness of the inner wall, n is the number of floors, P g1 is the outdoor atmospheric pressure at the center of the g-th floor of the building, P g2 It is the indoor atmospheric pressure at the center height of the g-th floor of the building.

5. The multi-objective optimization design method for prefabricated steel structure buildings according to claim 4 is characterized in that: The specific logic for obtaining the multi-objective building optimization relationship is: determine the optimization direction according to the building cost optimization relationship and the building energy consumption optimization relationship to obtain the multi-objective building optimization relationship. The specific formula is: Among them, F(k,d,h,n,l1,l2) is the multi-objective building optimization relation, L(k,d,h,n,l1,l2) is the building cost optimization relation, and En(k,d,h,n,l1,l2) is the building energy consumption optimization relation.

6. The multi-objective optimization design method for prefabricated steel structure buildings according to claim 1 is characterized in that: The specific logic for obtaining the feasible optimal building parameter solution set is: calculating the building structure stability of each optimal solution in the optimal building parameter solution set; Compare the building structure stability with the preset building structure stability threshold, judge the solution whose building structure stability performance is less than the building structure stability threshold as unsatisfactory, eliminate the optimal solution whose building structure stability is unsatisfactory, and obtain the feasible optimal building parameter solution set; The specific formula for calculating the stability of building structures is: Among them, Wd is the stability of the building structure, k is the total length of the building, d is the total width of the building, h is the total height of the building, l1 is the thickness of the outer wall, l2 is the thickness of the inner wall, n is the number of building floors, σ1 is the compressive strength of the steel material, τ is the shear strength of the steel material, We is the building weight, Cw is the wind load coefficient, and v is the local maximum historical wind speed.

7. The multi-objective optimization design method for prefabricated steel structure buildings according to claim 1 is characterized by: The specific logic for determining the optimal building parameters is as follows: calculate the construction cost and building energy consumption of each solution in the feasible optimal building parameter solution set, obtain the solution with the minimum construction cost and building energy consumption, and use the smaller building parameter of these two solutions to form the ideal optimal solution. Calculate the ideal distance between each solution in the feasible optimal building parameter solution set and the ideal optimal solution, and define the building parameters in the solution with the smallest ideal distance as the optimal building parameters. The specific formula for calculating the ideal distance is: Among them, DJ t is the ideal distance of the tth feasible optimal building parameter solution, k t is the total length of the building for the tth feasible optimal building parameter solution, d t is the total width of the building for the tth feasible optimal building parameter solution, h t is the total building height of the tth feasible optimal building parameter solution, l 1t is the thickness of the outer wall of the tth feasible optimal building parameter solution, l 2t is the thickness of the inner wall of the tth feasible optimal building parameter solution, n t is the number of floors of the tth feasible optimal building parameter solution, k0 is the total length of the building of the ideal optimal solution, d0 is the total width of the building of the ideal optimal solution, h0 is the total height of the building of the ideal optimal solution, l 10 is the thickness of the outer wall of the ideal optimal solution, l 20 is the thickness of the inner wall of the ideal optimal solution, and n0 is the number of building floors of the ideal optimal solution.

8. A multi-objective optimization design system for assembled steel structure buildings, characterized by: The system is used to implement any step of the multi-objective optimization design method for prefabricated steel structure buildings described in claims 1-7, specifically including: Framework modeling module: used to automatically generate prefabricated steel structure buildings that meet specific goals by adjusting different building lengths, building widths, and building heights. By adjusting the number of building floors, outer wall thickness, and inner wall thickness, the apartment layout of the entire prefabricated steel structure is generated. By assigning beam and column material properties, the BIM model of the steel frame is obtained. Relationship building module: used to establish relationships between the total length, total width, total height, number of floors, outer wall thickness, inner wall thickness, construction cost, and construction energy consumption, respectively, to obtain construction cost optimization relationship and building energy consumption optimization relationship; Parameter solving module: used to determine the optimization direction according to the building cost optimization relation and the building energy consumption optimization relation to obtain the multi-objective building optimization relation, and use the multi-objective genetic algorithm NSGA-II to solve the multi-objective building optimization relation to obtain the optimal building parameter solution set; Stability analysis module: used to calculate the building structure stability of each optimal solution in the optimal building parameter solution set; by comparing the building structure stability with the preset threshold, the optimal solution with substandard building structure stability is eliminated to obtain a feasible optimal building parameter solution set; Parameter determination module: used to determine the ideal optimal solution based on the feasible optimal building parameter solution set, calculate the ideal distance between each solution in the feasible optimal building parameter solution set and the ideal optimal solution, and determine the optimal building parameters based on the ideal distance.

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